Feature extraction within images holds a multitude of uses over multiple industries. For example, residential and/or commercial property owners approaching a major roofing project may be unsure of the amount of material needed and/or the next step in completing the project. Generally, such owners contact one or more contractors for a site visit. Each contractor must physically be present at the site of the structure in order to make a determination on material needs and/or time. The time and energy for providing such an estimate becomes laborious and may be affected by contractor timing, weather, contractor education, and the like. Estimates may be varied even between contractors in determination of estimated square footage causing variance in supply ordering as well. Additionally, measuring an actual roof may be costly and potentially hazardous—especially with steeply pitched roofs. Completion of a proposed roofing project may depend on ease in obtaining a simplified roofing estimate and/or obtaining reputable contractors for the roofing project.
Remote sensing technology has the ability to be more cost effective than manual inspection while providing pertinent information for assessment of roofing projects. Images are currently being used to measure objects and structures within the images, as well as to be able to determine geographic locations of points within the image when preparing estimates for a variety of construction projects, such as roadwork, concrete work, and roofing. See, for example, U.S. Pat. No. 7,424,133 that describes techniques for measuring within oblique images. Also see, for example, U.S. Pat. No. 8,145,578 that describe techniques for allowing the remote measurements of the size, geometry, pitch and orientation of the roof sections of the building and uses of the information to provide an estimate to repair or replace the roof, or to install equipment thereon. Estimating construction projects using software increases the speed at which an estimate is prepared, and reduces labor and fuel costs associated with on-site visits.
The identification of elements or features within an image, or even absent from an image, provides valuable information. Prior art methods of identification, however, waste time and energy, in addition to having variances between human extractors.
Prior methods of feature extraction and three-dimensional modeling from images required significant human interaction to view features within images depicting the roof and select points to form a wire-frame. Once the three-dimensional model is created, such model must also be sufficient precise in order to comply with geometric requirements of systems for viewing and/or manipulating such models. For example, three dimensional model representations transferred into a a general purpose CAD system or software likely require the vertex of a polygon exist to in the same plane only allowing for minimal tolerance. Generally, a technician imposes this coplanar requirement subsequent to formation of the three dimensional model from the images. For example, a three dimensional representation of an object is created, and then the system attempts to force the three-dimensional representation to be coplanar. Such methods, however, may warp the three-dimensional representation, and in addition, may require significant human interaction in order to create a three-dimensional representation that conforms with tolerance levels required for a CAD model.
In other prior art methods, systems require a structuring approach (i.e., Bottom-up modeling) that increases levels of abstraction from lower levels. For example, the methods described by Chen in U.S. Pat. No. 7,133,551, uses increased levels of abstraction from lower levels to form an object. The bottom-up modeling starts with segments that form polygons, polygons then form primitives, and the primitives are merged. With such methodology, however, enough segments must exist or be identified in order to perform an accurate structure process.